Miguel Anjos

Professor and Chair of Operational Research



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Miguel Anjos

Professor and Chair of Operational Research




Miguel Anjos

Professor and Chair of Operational Research



Optimal allocation of demand response considering transmission system congestion


Journal article


V.N. Motta, M.F. Anjos, M. Gendreau
Computational Management Science, vol. 20, 2023


Semantic Scholar DBLP DOI
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APA   Click to copy
Motta, V. N., Anjos, M. F., & Gendreau, M. (2023). Optimal allocation of demand response considering transmission system congestion. Computational Management Science, 20. https://doi.org/10.1007/s10287-023-00456-0


Chicago/Turabian   Click to copy
Motta, V.N., M.F. Anjos, and M. Gendreau. “Optimal Allocation of Demand Response Considering Transmission System Congestion.” Computational Management Science 20 (2023).


MLA   Click to copy
Motta, V. N., et al. “Optimal Allocation of Demand Response Considering Transmission System Congestion.” Computational Management Science, vol. 20, 2023, doi:10.1007/s10287-023-00456-0.


BibTeX   Click to copy

@article{v2023a,
  title = {Optimal allocation of demand response considering transmission system congestion},
  year = {2023},
  journal = {Computational Management Science},
  volume = {20},
  doi = {10.1007/s10287-023-00456-0},
  author = {Motta, V.N. and Anjos, M.F. and Gendreau, M.}
}

Abstract

The increasing penetration of renewable energy sources in the electricity grid brings new operational challenges. This brings up the need for effective means to provide demand response in spite of its distributed nature throughout the grid. Aggregators can be created to manage a set of such demand response resources, but deciding how to allocate an aggregator’s resources is an important problem. One of the aspects that needs more attention is the impact of the transmission system on these decisions. In this paper, we propose a short-term optimization model for allocating demand response(DR) resources as well as generation resources to supply external demand that is offered after the scheduling decision is made. The DR resources will only be available for use after the scheduling decision is made. Finally, our work also considers the impact of congestion in the transmission system when allocating DR. We propose the use of a semidefinite relaxation to provide a good initial point to solve our model with the aim of guaranteeing that we will find an optimal solution. Results from numerical tests with the IEEE 96-RTS and the ACTIVSG500 test grids are reported. We found that DR resources mitigates congestion management, allowing the generators to supply more of the external demand that is offered. Besides that, we observe that using our proposed solution methodology, we were able to obtain optimal solution for both case studies, which is not the case when solving the original formulation for the ACTIVSG500 grid.




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